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Haijing Sui,1,&ast; Hong Shen,1,&ast; Chi Zhang,1 Minghui Wang,1 Zhen Zhen,1 Junbo Zhang2 1Department of Otolaryngology, Head and Neck Surgery, Peking University First Hospital, Beijing, People’s Republic of China; 2Department of Otolaryngology, Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Junbo Zhang, Department of Otolaryngology, Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, People’s Republic of China, Email zhangjunbo354@163.com Zhen Zhen, Department of Otolaryngology, Head and Neck Surgery, Peking University First Hospital, Beijing, People’s Republic of China, Email zhen_ent@163.comObjective: This study aimed to explore whether saliva pepsin concentration (SPC) could be regarded as a risk factor for the occurrence and unfavorable control of asthma in children with allergic rhinitis.Methods: A prospective study was conducted on a group of 20 consecutive children newly diagnosed with allergic rhinitis and asthma (referred to as the asthma group). All these children underwent fractional exhaled nitric oxide (FeNO) measurement, lung function tests, and assessment of asthma control using the 7-item Childhood Asthma Control Test (C-ACT) score. Simultaneously, a control group consisting of 20 children with simple allergic rhinitis, matched for baseline characteristics, was included. SPC measurement was performed in the two groups.Results: The SPC value was significantly higher in the asthma group than that in the control group (165.0 ± 82.8 ng/mL vs 68.4 ± 34.5 ng/mL) (P < 0.001). In the asthma group, SPC was independently associated with FeNO, the ratio of forced expiratory volume in 1 second (FEV1) to forced vital capacity (FVC), and forced expiratory flow at 50% and 75% of FVC (FEF50 and FEF75) (all P

Hongmei Yang,1,&ast; Zhongqi Liu,1,&ast; Fan Liu,1,2,&ast; Haixuan Wu,1 Xiaoyan Huang,1 Rong Huang,1 Phei Er Saw,3,4 Minghui Cao1,2 1Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, People’s Republic of China; 2Department of Anesthesiology, Shenshan Medical Center, Sun Yat-Sen Memorial Hospital, Sun Yat-sen University, Shanwei, 516600, People’s Republic of China; 3Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, People’s Republic of China; 4Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Phei Er Saw; Minghui Cao, Email caipeie@mail.sysu.edu.cn; caomh@mail.sysu.edu.cnBackground: Opioids are irreplaceable analgesics owing to the lack of alternative analgesics that offer opioid-like pain relief. However, opioids have many undesirable central side effects. Restricting opioids to peripheral opioid receptors could reduce those effects while maintaining analgesia.Methods: To achieve this goal, we developed Tet1-LNP (morphine), a neural-targeting lipid nanoparticle encapsulating morphine that could specifically activate the peripheral opioid receptor in the dorsal root ganglion (DRG) and significantly reduce the side effects caused by the activation of opioid receptors in the brain. Tet1-LNP (morphine) were successfully prepared using the thin-film hydration method. In vitro, Tet1-LNP (morphine) uptake was assessed in differentiated neuron-like PC-12 cells and dorsal root ganglion (DRG) primary cells. The uptake of Tet1-LNP (morphine) in the DRGs and the brain was assessed in vivo. Von Frey filament and Hargreaves tests were used to assess the antinociception of Tet1-LNP (morphine) in the chronic constriction injury (CCI) neuropathic pain model

Breast cancer metastasis to the brain is a clinical challenge rising in prevalence. However, the underlying mechanisms, especially how cancer cells adapt a distant brain niche to facilitate colonization, remain poorly understood. A unique metabolic feature of the brain is the coupling between neurons and astrocytes through glutamate, glutamine, and lactate. Here we show that extracellular vesicles from breast cancer cells with a high potential to develop brain metastases carry high levels of miR-199b-5p, which shows higher levels in the blood of breast cancer patients with brain metastases comparing to those with metastatic cancer in other organs. miR-199b-5p targets solute carrier transporters (SLC1A2/EAAT2 in astrocytes and SLC38A2/SNAT2 and SLC16A7/MCT2 in neurons) to hijack the neuron-astrocyte metabolic coupling, leading to extracellular retention of these metabolites and promoting cancer cell growth. Our findings reveal a mechanism through which cancer cells of a non-brain origin reprogram neural metabolism to fuel brain metastases.

Se ha creado un espacio universitario donde, gracias a destacados especialistas en paz, desarrollo, seguridad y educación transformadora, se han aportado enfoques y prácticas para afrontar los desafíos de la convivencia social e impulsar la cultura de la paz, todo ello muy necesario para la formación inicial de maestros y maestras. Por otro lado, se ha facilitado y experimentado una propuesta didáctica orientada a promover el dialogo y respeto mutuo en el ámbito de la Didáctica de la Química. Finalmente, se ha llevado a cabo un estudio exploratorio sobre la actitud de los futuros docentes (estudiantes de la Facultad de Educación - Centro de Formación del Profesorado de la Universidad Complutense de Madrid) hacia la paz y la guerra.

Siyuan Wang,1,&ast; Beiyao Gao,1,&ast; Minghui Shi,2– 6 Shiwei Qumu,2– 5 Fen Dong,2– 5,7 Peijian Wang,1 Ting Yang,2– 5 Shan Jiang1 1Department of Rehabilitation Medicine, China-Japan Friendship Hospital, Beijing, People’s Republic of China; 2National Center for Respiratory Medicine, Beijing, People’s Republic of China; 3National Clinical Research Center for Respiratory Diseases, Beijing, People’s Republic of China; 4Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Beijing, People’s Republic of China; 5Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, People’s Republic of China; 6Capital Medical University, Beijing, People’s Republic of China; 7Department of Clinical Research and Data management, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Shan Jiang; Ting Yang, China-Japan Friendship Hospital, 2 Yinghuayuan East St. Hepingli, Chaoyang District, Beijing, 100029, People’s Republic of China, Email landjiang@126.com; dryangting@qq.comObjective: Given the established impact of exercise in reducing arterial stiffness and the potential for intermittent hypoxia to induce its elevation, this study aims to understand how oxygen desaturation during exercise affects arterial stiffness in individuals with COPD.Methods: We enrolled patients with stable COPD from China-Japan Friendship Hospital from November 2022 to June 2023. The 6-minute walk test (6-MWT) was performed with continuous blood oxygen saturation (SpO2) monitoring in these patients. The patients were classified into three groups: non-exercise induced desaturation (EID), mild-EID and severe-EID, according to the changes in SpO2 during the 6-MWT. The Cardio-Ankle Vascular Index (CAVI) and the change in CAVI (ΔCAVI, calculated as CAVI before 6MWT minus CAVI after th

Through the development of microreactors, a free piston Stirling engine (FPSE) is an excellent candidate to support the development of the microreactors applications. This study proposes a 20 kWe design of the FPSE for microreactor applications. The model has been developed and analyzed through a MATLAB code that was verified and benchmarked with both RE-1000 engine’s experiment data and Sage software. The MATLAB code has been developed from scratch using the first and second order approximation to simulate the engine’s steady state operation. There are three operating conditions that were applied to the code such as ideal isothermal, ideal adiabatic, and non-ideal adiabatic. Through the process, a design of 20.7 kWe with the efficiency of 21.7 percent has been proposed. Overall, this study provides a theoretical support for design and analysis of the FPSE for microreactor applications.

Macroautophagy/autophagy is a complex degradation process with a dual role in cell death that is influenced by the cell types that are involved and the stressors they are exposed to. Ferroptosis is an iron-dependent oxidative form of cell death characterized by unrestricted lipid peroxidation in the context of heterogeneous and plastic mechanisms. Recent studies have shed light on the involvement of specific types of autophagy (e.g. ferritinophagy, lipophagy, and clockophagy) in initiating or executing ferroptotic cell death through the selective degradation of anti-injury proteins or organelles. Conversely, other forms of selective autophagy (e.g. reticulophagy and lysophagy) enhance the cellular defense against ferroptotic damage. Dysregulated autophagy-dependent ferroptosis has implications for a diverse range of pathological conditions. This review aims to present an updated definition of autophagy-dependent ferroptosis, discuss influential substrates and receptors, outline experimental methods, and propose guidelines for interpreting the results. Abbreviation: 3-MA:3-methyladenine; 4HNE: 4-hydroxynonenal; ACD: accidentalcell death; ADF: autophagy-dependentferroptosis; ARE: antioxidant response element; BH2:dihydrobiopterin; BH4: tetrahydrobiopterin; BMDMs: bonemarrow-derived macrophages; CMA: chaperone-mediated autophagy; CQ:chloroquine; DAMPs: danger/damage-associated molecular patterns; EMT,epithelial-mesenchymal transition; EPR: electronparamagnetic resonance; ER, endoplasmic reticulum; FRET: Försterresonance energy transfer; GFP: green fluorescent protein;GSH: glutathione;IF: immunofluorescence; IHC: immunohistochemistry; IOP, intraocularpressure; IRI: ischemia-reperfusion injury; LAA: linoleamide alkyne;MDA: malondialdehyde; PGSK: Phen Green™ SK;RCD: regulatedcell death; PUFAs: polyunsaturated fatty acids; RFP: red fluorescentprotein;ROS: reactive oxygen species; TBA: thiobarbituricacid; TBARS: thiobarbituric acid reactive substances; TEM:transmission elec

The three-dimensional (3D) organization of genome is fundamental to cell biology. To explore 3D genome, emerging high-throughput approaches have produced billions of sequencing reads, which is challenging and time-consuming to analyze. Here we present Microcket, a package for mapping and extracting interacting pairs from 3D genomics data, including Hi-C, Micro-C, and derivant protocols. Microcket utilizes a unique read-stitch strategy that takes advantage of the long read cycles in modern DNA sequencers; benchmark evaluations reveal that Microcket runs much faster than the current tools along with improved mapping efficiency, and thus shows high potential in accelerating and enhancing the biological investigations into 3D genome. Microcket is freely available at https://github.com/hellosunking/Microcket. Introducing Microcket, a versatile toolkit for read alignment and interacting pair extraction from 3D genomics data (including Hi-C and Micro-C) with improved speed and sensitivity.

Objective Social media-based public health research is crucial for epidemic surveillance, but most studies identify relevant corpora with keyword-matching. This study develops a system to streamline the process of curating colloquial medical dictionaries. We demonstrate the pipeline by curating a Unified Medical Language System (UMLS)-colloquial symptom dictionary from COVID-19-related tweets as proof of concept.Methods COVID-19-related tweets from February 1, 2020, to April 30, 2022 were used. The pipeline includes three modules: a named entity recognition module to detect symptoms in tweets; an entity normalization module to aggregate detected entities; and a mapping module that iteratively maps entities to Unified Medical Language System concepts. A random 500 entity samples were drawn from the final dictionary for accuracy validation. Additionally, we conducted a symptom frequency distribution analysis to compare our dictionary to a pre-defined lexicon from previous research.Results We identified 498 480 unique symptom entity expressions from the tweets. Pre-processing reduces the number to 18 226. The final dictionary contains 38 175 unique expressions of symptoms that can be mapped to 966 UMLS concepts (accuracy = 95%). Symptom distribution analysis found that our dictionary detects more symptoms and is effective at identifying psychiatric disorders like anxiety and depression, often missed by pre-defined lexicons.Conclusions This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data. The final lexicon's high accuracy, validated by medical professionals, underscores the potential of this methodology to reliably interpret, and categorize vast amounts of unstructured social media data into actionable medical insights across diverse linguistic and regional landscapes.

Over recent decades, the adverse impacts of airborne particles on human health have received wide attention. Elevated PM concentrations on underground platforms might pose a significant public health issue within underground metro systems. This study explores the impact of introducing a new type of train on the concentration of airborne particles on an underground metro platform through statistical modelling, analyses interactions between various factors, and estimates air quality on underground platforms after introducing a new type of train. Based on the data from a long-term field measurement, a linear mixed model, the multi-factor interaction model, which is an expansion of a previous multi-factor model, explored the impacts of train operations, passenger flow, urban background PM levels, ventilation, nighttime maintenance work, and their interactions on hourly PM10, PM2.5, and PM1 values on the platform. The model results show a positive correlation between those factors and platform PM10, PM2.5 and PM1 values, with significant interactions among these factors. The new model has a higher estimate quality than the previous model. Based on the combination of the model and measurement results, the levels of underground PM decreased significantly after replacing the old type of trains with new ones., QC 20240603

Non-exhaust sources, such as brakes, tyres, roads, and clutches, emit a large portion of airborne particles in road transportation, from ultrafine to coarse sizes. While airborne wear particle emissions from brakes and road-tyre contacts have been studied extensively, emissions from clutches have been overlooked. A preliminary study using a novel test rig has indicated that dry clutches also emit airborne wear particles. This paper presents a multi-method for the assessment of ultrafine particles from dry clutches regarding the number concentration, size distribution and chemical composition. The results show that ultrafine particles are emitted both during run-in and at the steady state, featuring a bi-modal size distribution. Elementary analysis shows that the particles consist of several elements, predominately iron, silicon, and sulfur. It can be concluded from this study that ultrafine particles are always generated when the clutch is operated. Clutches in automobiles are shown to produce ultrafine particles, consisting of several metallic elements, which become airborne., QC 20240408

Jinhui Bu,1,2,&ast; Zhenfei Wang,2,&ast; Chao Ma,1,2 Juan Gao,2 Guangpu Liu,2 Libo Pang,2 Bo He,1 Minghui Dong,1 Quan Zhang,1 Yan Lei,1 Long Xu,2 Sen Huang,1 Yuming Li,3 Guangwang Liu1,2 1Xuzhou Clinical School of Xuzhou Medical University, Xuzhou, People’s Republic of China; 2Department of Orthopedic Surgery, Xuzhou Central Hospital, Xuzhou, People’s Republic of China; 3Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA&ast;These authors contributed equally to this workCorrespondence: Yuming Li, Tel +1-8573524988, Fax +1-8572188459, Email yuming.li@childrens.harvard.edu; Guangwang Liu, Tel +86-18012018238, Fax +86-51683956400, Email guangwangliu@163.comObjective: To validate whether a residual mass demonstrated on early postoperative MR after percutaneous endoscopic lumbar discectomy (PELD) is indeed an intraoperatively retained annulus fibrosus, and explore the correlation between imaging changes in the residual mass and clinical prognosis of patients.Methods: A prospective study of 118 patients were included. During surgery, a contrast medium, Gadopentetate Dimeglumine, was injected around the ruptured annulus fibrosus. The intensity of the T2 signal, the size of the remaining mass (SR), and the cross-sectional area of the spinal canal (SCSA), VAS, and ODI were assessed at preoperative, 1-h (7-day), 6-month, and 12-month postoperative intervals. Based on VAS at 7 days post-surgery, patients were classified into either a non-remission group (Group A, VAS > 3) or a remission group (Group B, VAS ≤ 3).Results: Six patients who developed recurrent LDH were excluded. A residual mass was detected on MRI 1 h after surgery in 94.6% (106/112). During one year of follow-up, 90.1% (101/112) of the patients displayed fibrous annulus remodeling, although 68.7% (77/112) still exhibited herniation. Significant differences were found in the ODI between Groups A and B one week after surgery (p < 0.001). However, no

Location privacy protection (LPP) has become a key concern during mobile crowdsourcing (MCS) task allocation. Existing LPP mechanisms for MCS applications mainly focus on two-dimensional (2D) plane scenarios or directly apply 2D techniques into three-dimensional (3D) space scenarios, leaving the height dimension of 3D geolocation vulnerable to privacy breaches. To facilitate the LPP in 3D MCS, we propose a learning-based geo-perturbation mechanism using 3D geo-indistinguishability (3D-GI). In this mechanism, we first define an optimization objective to balance location privacy and MCS server profit, making it adaptable to different types of MCS applications. Then, we adopt the Asynchronous Advantage Actor-Critic (A3C) algorithm to design a reinforcement learning (RL)-based approach without knowing the accurate system and attack models. This approach enables us to derive the optimal perturbation policy in continuous policy space and accelerates the learning speed using asynchronous multi-thread training. Simulation results demonstrate that the proposed mechanism can better balance location privacy and server profit in 3D MCS applications compared to existing benchmarks.

Due to the non-linearity in ozone (O3) formation, reducing the emission of nitrogen oxides (NOx) may increase O3 concentration. Given the counteractive O3 response to NOx reduction, overall impact of air pollution controls can be ambiguous when the assessments focus on the changes in pollutant concentrations. In this study, a risk-based method was used to gauge the net effect of air pollution controls on mortality risk in the Beijing–Tianjin–Hebei (BTH) region during the 2022 Winter Olympics and Paralympics (WOP). This mega-event presents a unique opportunity to investigate the efficacy of deep cuts in pollutant emissions. Results show that O3 concentrations greatly increased as nitrogen dioxide (NO2) concentrations decreased in the BTH. Due to the active photochemical formations, O3 became the dominant pollutant that affected human health during the WOP. Despite the substantial O3 increases, the health benefits of NO2 reductions overwhelmed the adverse health effects of O3 increases in most regions of the BTH (at 81 out of 112 stations). After considering the impacts of particulate matter, the integrated health risk of air pollution mixtures declined almost everywhere in the BTH. Our results underscore the great necessity of changing the assessment paradigm of pollution control from using concentration-based methods to using risk-based methods. Together with the carbon neutrality policy, stringent control of NOx emission from combustion sources is a promising way to achieve synergistic control solutions for air pollution and climate change. © 2022

Time Delay Interferometry (TDI) is an indispensable step in the whole data processing procedure of space-based gravitational wave detection, as it mitigates the overwhelming laser frequency noise, which would otherwise completely bury the gravitational wave signals. Knowledge on the inter-spacecraft optical paths (i.e. delays) is one of the key elements of TDI. Conventional method for inter-spacecraft ranging mainly relies on the pseudo-random noise (PRN) code signal modulated onto the lasers. To ensure the reliability and robustness of this ranging information, it would be highly beneficial to develop other methods which could serve as cross-validations or backups. This paper explores the practical implementation of an alternative data-driven approach - time delay interferometry ranging (TDIR) - as a ranging technique independent of the PRN signal. Distinguished from previous research, our TDIR algorithm significantly relaxes the stringent requirement for clock synchronization imposed by traditional TDI procedure. By framing TDIR as a Bayesian parameter estimation problem and employing a general polynomial parametrization, we demonstrate our algorithm with simulated data based on the numerical orbit of Taiji. In the presence of laser frequency noise and secondary noises, the estimated median values of delays are only 5.28 ns away from the ground truths, capable of suppressing laser frequency noise to the desired level. Additionally, we have also analysed the requirements of mitigating optical bench noise and clock noise on TDIR, and presented an illustrative example for the impact of laser locking.

Sapce-borne gravitational wave antennas, such as LISA and LISA-like mission (Taiji and Tianqin), will offer novel perspectives for exploring our Universe while introduce new challenges, especially in data analysis. Aside from the known challenges like high parameter space dimension, superposition of large number of signals and etc., gravitational wave detections in space would be more seriously affected by anomalies or non-stationarities in the science measurements. Considering the three types of foreseeable non-stationarities including data gaps, transients (glitches), and time-varying noise auto-correlations, which may come from routine maintenance or unexpected disturbances during science operations, we developed a deep learning model for accurate signal extractions confronted with such anomalous scenarios. Our model exhibits the same performance as the current state-of-the-art models do for the ideal and anomaly free scenario, while shows remarkable adaptability in extractions of coalescing massive black hole binary signal against all three types of non-stationarities and even their mixtures. This also provide new explorations into the robustness studies of deep learning models for data processing in space-borne gravitational wave missions., Comment: 11 pages, 10 figures, 5 tables

Jing Zhang,1,2 Ke Ma,3 Jun Yan,4 Pingsheng Pan,5 Jiangshan Yin,6 Fuqing Lin,7 Hui Chen,8 Weiwei Zhao,1,2 Menglei Chen,1,2 Zhe Zhang,1,2 Minghui Liu,1,2 Xiaoli Gu,1,2 Xinghe Liao,1,2 Chenyue Zhang,1,2 Wenwu Cheng1,2 1Department of Integrated Therapy, Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China; 2Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China; 3Department of Pain Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 4Department of Oncology, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, People’s Republic of China; 5Department of Oncology, Fangta Hospital of Traditional Chinese Medicine in Songjiang District, Shanghai, People’s Republic of China; 6Department of Oncology, Fudan University Shanghai Cancer Center, Minhang Branch, Shanghai, People’s Republic of China; 7Department of Anaesthesiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, People’s Republic of China; 8Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai, People’s Republic of ChinaCorrespondence: Wenwu Cheng, Email cwwxxm@sina.comPurpose: This study aimed to assess the current status of clinical practice of refractory cancer pain (RCP) among a sample of physicians specializing in cancer pain management in Shanghai.Methods: From 2019 to 2021, a questionnaire survey was conducted among physicians engaged in diagnosis and treatment of cancer pain through the questionnaire WJX network platform in Shanghai, China.Results: A total of 238 responses participated in the survey. This survey reports physicians’ understanding and incidence rate of breakthrough cancer pain (BTCP). The choice of analgesics and satisfaction of analgesic ef

Kaiyang Xue,1 Xianzhu Wang,1 Caihong Xiao,2 Ning Zhang,1 Minghui Liu,3 Jing Fu,1 Jin Cui1 1College of Acupuncture and Massage, Guizhou University of Traditional Chinese Medicine, Guiyang, 550002, People’s Republic of China; 2Department of Acupuncture and Moxibustion, No.1 Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, 550001, People’s Republic of China; 3Department of Acupuncture and Moxibustion, No.2 Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, 550001, People’s Republic of ChinaCorrespondence: Jin Cui, College of Acupuncture and Massage, Guizhou University of Traditional Chinese Medicine, No. 50 Shidong Road, Nanming District, Guiyang, 550002, People’s Republic of China, Email 516260179@qq.comObjective: Our pilot study shows that a modified moxibustion therapy called Ma’s bamboo-based medicinal moxibustion can alleviate the symptoms of low back pain in lumbar disc herniation (LDH), and has the potential to treat LDH. The aim of this study is to evaluate the efficacy and safety of Ma’s bamboo-based medicinal moxibustion for low back pain in LDH.Methods: A total of 312 LDH patients with low back pain were randomized to receive Ma’s bamboo-based medicinal moxibustion (MBMM) or acupuncture (AT). The primary efficacy measure was the change of Visual Analogue Scale (VAS) on the 14th day compared with that at baseline. The secondary efficacy measures included VAS score, Oswestry disability index (ODI), modified Japanese Orthopaedic Association (M-JOA) score, and the content of β-endorphin (β-EP) and substance-P (SP). The safety measures included the occurrence of adverse events and the changes in laboratory indicators.Results: In total, 304 patients were incorporated for the analysis of efficacy, including 96 males and 208 females, aged 21– 65 years. There was no statistically significant difference in the change of VAS score between the two groups on the 14th day [mean differenc

Taking the maximum contact pressure as the objective function for, the optimal design model of the offshore flange connector was established to analyze the impact of the flange cone's angle and the curvature radius of the lenticular gasket's contact surface on the sealing performance of the connector. An optimized three-dimensional model of the offshore flange connector was constructed using the MATLAB software's fmincon function to obtain the optimal size of the cone angle and curvature radius. The maximum contact pressure and maximum equivalent stress values of the non-optimized and optimized offshore flange connectors under the cross combination of two design pressures and six operating temperatures were analyzed by Workbench software, and the sealing performance of the non-optimized and optimized offshore flange connectors was compared according to the sealing judgment basis. The results show that compared with the previously studied offshore flange connector, the sealing structure of optimized offshore flange exhibits maximum increase in contact pressure increase but maximum decrease in equivalent stress. Under actual operating circumstances, the optimized offshore flange connection performs better in sealing and is less prone to breakage.

Yingfan Chen,1,2,&ast; Zhenhui Wu,3,&ast; Shaodan Li,2 Qi Chen,2 Liang Wang,2 Xiaorong Qi,1 Chujiao Tian,1 Minghui Yang2 1Medical School of Chinese People’s Liberation Army, Beijing, People’s Republic of China; 2Department of Traditional Chinese Medicine, the Six Medical Center of Chinese People’s Liberation Army General Hospital, Beijing, People’s Republic of China; 3College of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Minghui Yang, Department of Traditional Chinese Medicine, the Six Medical Center, Chinese People’s Liberation Army General Hospital, 6 Fucheng Road, Beijing, 100048, People’s Republic of China, Tel +86-10-66939651, Email ymh9651@sina.comMethods: Related studies on PD and ferroptosis were searched in Web of Science Core Collection (WOSCC) from inception to 2023. VOSviewer, CiteSpace, RStudio, and Scimago Graphica were employed as bibliometric analysis tools to generate network maps about the collaborations between authors, countries, and institutions and to visualize the co-occurrence and trends of co-cited references and keywords.Results: A total of 160 original articles and reviews related to PD and ferroptosis were retrieved, produced by from 958 authors from 162 institutions. Devos David was the most prolific author, with 9 articles. China and the University of Melbourne had leading positions in publication volume with 84 and 12 publications, respectively. Current hot topics focus on excavating potential new targets for treating PD based on ferroptosis by gaining insight into specific molecular mechanisms, including iron metabolism disorders, lipid peroxidation, and imbalanced antioxidant regulation. Clinical studies aimed at treating PD by targeting ferroptosis remain in their preliminary stages.Conclusion: A continued increase was shown in the literature within the related field over the past decade. The curren

This paper explores how parents and teachers view and do parental involvement in elementary schools. It uses surveys and interviews with parents and teachers from two schools. It finds that both groups value parental involvement and its positive effects on students’ learning and happiness. But it also identifies some problems that prevent good parental involvement, like time, communication, resources, and training. It suggests some ways to improve parental involvement and cooperation between parents and teachers.

Ren F, Shrestha C, Shi H, et al. Onco Targets Ther. 2020;13;9419-9428. At the authors request, we, the Editor and Publisher of the journal OncoTargets and Therapy, have retracted the following article. Following publication of the article, concerns were raised about the duplication of images from another article authored by the first author, Fang Ren. Specifically, The image for Figure 4H, OVCAR-8, miR-421 mimics, has been duplicated with an image for Figure 4F, MDA-MB-231, anti-miR-374a, from Zhang J, He Y, Yu Y, et al. Upregulation of miR-374a promotes tumor metastasis and progression by downregulating LACTB and predicts unfavorable prognosis in breast cancer. Cancer Med. 2018;7:3351–3362. https://doi.org/10.1002/cam4.1576. In addition, images for Figure 5D, Lenti-NC group and Lenti-miR-421, Ki-67 have been duplicated. The authors responded to our queries but were unable to provide a satisfactory explanation for the duplicated images or provide satisfactory data for the study. As verifying the validity of published work is core to the integrity of the scholarly record, the authors requested to retract the article and we, the editor and publisher, agreed with this decision. We have been informed in our decision-making by our editorial policies and COPE guidelines. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.

To achieve the physics goal of precisely measure the Higgs, Z, W bosons and the top quark, future electron-positron colliders require that their detector system has excellent jet energy resolution. One feasible technical option is the high granular calorimetery based on the particle flow algorithm (PFA). A new high-granularity hadronic calorimeter with glass scintillator tiles (GSHCAL) has been proposed, which focus on the significant improvement of hadronic energy resolution with a notable increase of the energy sampling fraction by using high-density glass scintillator tiles. The minimum ionizing particle (MIP) response of a glass scintillator tile is crucial to the hadronic calorimeter, so a dedicated beamtest setup was developed for testing the first batch of large-size glass scintillators. The maximum MIP response of the first batch of glass scintillator tiles can reach up to 107 p.e./MIP, which essentially meets the design requirements of the CEPC GSHCAL. An optical simulation model of a single glass scintillator tile has been established, and the simulation results are consistent with the beamtest results.

Headline generation aims to summarize a long document with a short, catchy title that reflects the main idea. This requires accurately capturing the core document semantics, which is challenging due to the lengthy and background information-rich na ture of the texts. In this work, We propose using a unified semantic discourse structure (S3) to represent document semantics, achieved by combining document-level rhetorical structure theory (RST) trees with sentence-level abstract meaning representation (AMR) graphs to construct S3 graphs. The hierarchical composition of sentence, clause, and word intrinsically characterizes the semantic meaning of the overall document. We then develop a headline generation framework, in which the S3 graphs are encoded as contextual features. To consolidate the efficacy of S3 graphs, we further devise a hierarchical structure pruning mechanism to dynamically screen the redundant and nonessential nodes within the graph. Experimental results on two headline generation datasets demonstrate that our method outperforms existing state-of-art methods consistently. Our work can be instructive for a broad range of document modeling tasks, more than headline or summarization generation.

Decentralized Storage Network (DSN) is an emerging technology that challenges traditional cloud-based storage systems by consolidating storage capacities from independent providers and coordinating to provide decentralized storage and retrieval services. However, current DSNs face several challenges associated with data privacy and efficiency of the proof systems. To address these issues, we propose FileDES (\uline{D}ecentralized \uline{E}ncrypted \uline{S}torage), which incorporates three essential elements: privacy preservation, scalable storage proof, and batch verification. FileDES provides encrypted data storage while maintaining data availability, with a scalable Proof of Encrypted Storage (PoES) algorithm that is resilient to Sybil and Generation attacks. Additionally, we introduce a rollup-based batch verification approach to simultaneously verify multiple files using publicly verifiable succinct proofs. We conducted a comparative evaluation on FileDES, Filecoin, Storj and Sia under various conditions, including a WAN composed of up to 120 geographically dispersed nodes. Our protocol outperforms the others in terms of proof generation/verification efficiency, storage costs, and scalability., Comment: 10 pages, 8 figures, 1 table. Accepted by 2024 IEEE INFOCOM

In recent years, emerging research on mobile sensing has led to novel scenarios that enhance daily life for humans, but dynamic usage conditions often result in performance degradation when systems are deployed in real-world settings. Existing solutions typically employ one-off adaptation schemes based on neural networks, which struggle to ensure robustness against uncertain drifting conditions in human-centric sensing scenarios. In this paper, we propose CODA, a COst-efficient Domain Adaptation mechanism for mobile sensing that addresses real-time drifts from the data distribution perspective with active learning theory, ensuring cost-efficient adaptation directly on the device. By incorporating a clustering loss and importance-weighted active learning algorithm, CODA retains the relationship between different clusters during cost-effective instance-level updates, preserving meaningful structure within the data distribution. We also showcase its generalization by seamlessly integrating it with Neural Network-based solutions for Human Activity Recognition tasks. Through meticulous evaluations across diverse datasets, including phone-based, watch-based, and integrated sensor-based sensing tasks, we demonstrate the feasibility and potential of online adaptation with CODA. The promising results achieved by CODA, even without learnable parameters, also suggest the possibility of realizing unobtrusive adaptation through specific application designs with sufficient feedback.

This paper considers the problem of learning a control policy for robot motion planning with zero-shot generalization, i.e., no data collection and policy adaptation is needed when the learned policy is deployed in new environments. We develop a federated reinforcement learning framework that enables collaborative learning of multiple learners and a central server, i.e., the Cloud, without sharing their raw data. In each iteration, each learner uploads its local control policy and the corresponding estimated normalized arrival time to the Cloud, which then computes the global optimum among the learners and broadcasts the optimal policy to the learners. Each learner then selects between its local control policy and that from the Cloud for next iteration. The proposed framework leverages on the derived zero-shot generalization guarantees on arrival time and safety. Theoretical guarantees on almost-sure convergence, almost consensus, Pareto improvement and optimality gap are also provided. Monte Carlo simulation is conducted to evaluate the proposed framework.

Realizing consumer-grade drones that are as useful as robot vacuums throughout our homes or personal smartphones in our daily lives requires drones to sense, actuate, and respond to general scenarios that may arise. Towards this vision, we propose RASP, a modular and reconfigurable sensing and actuation platform that allows drones to autonomously swap onboard sensors and actuators in only 25 seconds, allowing a single drone to quickly adapt to a diverse range of tasks. RASP consists of a mechanical layer to physically swap sensor modules, an electrical layer to maintain power and communication lines to the sensor/actuator, and a software layer to maintain a common interface between the drone and any sensor module in our platform. Leveraging recent advances in large language and visual language models, we further introduce the architecture, implementation, and real-world deployments of a personal assistant system utilizing RASP. We demonstrate that RASP can enable a diverse range of useful tasks in home, office, lab, and other indoor settings.

This is only a preview version of GauMesh. Recently, primitive-based rendering has been proven to achieve convincing results in solving the problem of modeling and rendering the 3D dynamic scene from 2D images. Despite this, in the context of novel view synthesis, each type of primitive has its inherent defects in terms of representation ability. It is difficult to exploit the mesh to depict the fuzzy geometry. Meanwhile, the point-based splatting (e.g. the 3D Gaussian Splatting) method usually produces artifacts or blurry pixels in the area with smooth geometry and sharp textures. As a result, it is difficult, even not impossible, to represent the complex and dynamic scene with a single type of primitive. To this end, we propose a novel approach, GauMesh, to bridge the 3D Gaussian and Mesh for modeling and rendering the dynamic scenes. Given a sequence of tracked mesh as initialization, our goal is to simultaneously optimize the mesh geometry, color texture, opacity maps, a set of 3D Gaussians, and the deformation field. At a specific time, we perform $\alpha$-blending on the RGB and opacity values based on the merged and re-ordered z-buffers from mesh and 3D Gaussian rasterizations. This produces the final rendering, which is supervised by the ground-truth image. Experiments demonstrate that our approach adapts the appropriate type of primitives to represent the different parts of the dynamic scene and outperforms all the baseline methods in both quantitative and qualitative comparisons without losing render speed., Comment: 7 pages

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the attackers. In this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Our extensive experiments, conducted across ten self-supervised training methods and six datasets, demonstrate that Gen-AF attains high testing accuracy and robust testing accuracy against state-of-the-art DAEs.

In the era of Foundation Models' (FMs) rising prominence in AI, our study addresses the challenge of biases in medical images while using FM API, particularly spurious correlations between pixels and sensitive attributes. Traditional methods for bias mitigation face limitations due to the restricted access to web-hosted FMs and difficulties in addressing the underlying bias encoded within the FM API. We propose an U(niversal) D(ebiased) E(diting) strategy, termed UDE, which generates UDE noise to mask such spurious correlation. UDE is capable of mitigating bias both within the FM API embedding and the images themselves. Furthermore, UDE is suitable for both white-box and black-box FM APIs, where we introduced G(reedy) (Z)eroth-O(rder) (GeZO) optimization for it when the gradient is inaccessible in black-box APIs. Our whole pipeline enables fairness-aware image editing that can be applied across various medical contexts without requiring direct model manipulation or significant computational resources. Our empirical results demonstrate the method's effectiveness in maintaining fairness and utility across different patient groups and diseases. In the era of AI-driven medicine, this work contributes to making healthcare diagnostics more equitable, showcasing a practical solution for bias mitigation in pre-trained image FMs.

Large language models (LLMs) are complex artificial intelligence systems capable of understanding, generating and translating human language. They learn language patterns by analyzing large amounts of text data, allowing them to perform writing, conversation, summarizing and other language tasks. When LLMs process and generate large amounts of data, there is a risk of leaking sensitive information, which may threaten data privacy. This paper concentrates on elucidating the data privacy concerns associated with LLMs to foster a comprehensive understanding. Specifically, a thorough investigation is undertaken to delineate the spectrum of data privacy threats, encompassing both passive privacy leakage and active privacy attacks within LLMs. Subsequently, we conduct an assessment of the privacy protection mechanisms employed by LLMs at various stages, followed by a detailed examination of their efficacy and constraints. Finally, the discourse extends to delineate the challenges encountered and outline prospective directions for advancement in the realm of LLM privacy protection., Comment: 18 pages, 4 figures

Large language model (LLM) leads to a surge of autonomous GUI agents for smartphone, which completes a task triggered by natural language through predicting a sequence of actions of API. Even though the task highly relies on past actions and visual observations, existing studies typical consider little semantic information carried out by intermediate screenshots and screen operations. To address this, this work presents Chain-of-Action-Thought (dubbed CoAT), which takes the description of the previous actions, the current screen, and more importantly the action thinking of what actions should be performed and the outcomes led by the chosen action. We demonstrate that, in a zero-shot setting upon an off-the-shell LLM, CoAT significantly improves the goal progress compared to standard context modeling. To further facilitate the research in this line, we construct a benchmark Android-In-The-Zoo (AitZ), which contains 18,643 screen-action pairs together with chain-of-action-thought annotations. Experiments show that fine-tuning a 200M model on our AitZ dataset achieves on par performance with CogAgent-Chat-18B., Comment: Dataset could be found in https://github.com/IMNearth/CoAT

Latent Consistency Model (LCM) extends the Consistency Model to the latent space and leverages the guided consistency distillation technique to achieve impressive performance in accelerating text-to-image synthesis. However, we observed that LCM struggles to generate images with both clarity and detailed intricacy. Consequently, we introduce Trajectory Consistency Distillation (TCD), which encompasses trajectory consistency function and strategic stochastic sampling. The trajectory consistency function diminishes the parameterisation and distillation errors by broadening the scope of the self-consistency boundary condition with trajectory mapping and endowing the TCD with the ability to accurately trace the entire trajectory of the Probability Flow ODE in semi-linear form with an Exponential Integrator. Additionally, strategic stochastic sampling provides explicit control of stochastic and circumvents the accumulated errors inherent in multi-step consistency sampling. Experiments demonstrate that TCD not only significantly enhances image quality at low NFEs but also yields more detailed results compared to the teacher model at high NFEs., Comment: Project Page: https://mhh0318.github.io/tcd

As one of the most popular parameter-efficient fine-tuning (PEFT) methods, low-rank adaptation (LoRA) is commonly applied to fine-tune large language models (LLMs). However, updating the weights of LoRA blocks effectively and expeditiously is challenging due to the long calculation path in the original model. To address this, we propose ResLoRA, an improved framework of LoRA. By adding residual paths during training and using merging approaches to eliminate these extra paths during inference, our method can achieve better results in fewer training steps without any extra trainable parameters or inference cost compared to LoRA. The experiments on NLG, NLU, and text-to-image tasks demonstrate the effectiveness of our method. To the best of our knowledge, ResLoRA is the first work that combines the residual path with LoRA. The code of our method is available at https://github.com/microsoft/LMOps/tree/main/reslora ., Comment: 14 pages, 7 figures

Scene text recognition is a rapidly developing field that faces numerous challenges due to the complexity and diversity of scene text, including complex backgrounds, diverse fonts, flexible arrangements, and accidental occlusions. In this paper, we propose a novel approach called Class-Aware Mask-guided feature refinement (CAM) to address these challenges. Our approach introduces canonical class-aware glyph masks generated from a standard font to effectively suppress background and text style noise, thereby enhancing feature discrimination. Additionally, we design a feature alignment and fusion module to incorporate the canonical mask guidance for further feature refinement for text recognition. By enhancing the alignment between the canonical mask feature and the text feature, the module ensures more effective fusion, ultimately leading to improved recognition performance. We first evaluate CAM on six standard text recognition benchmarks to demonstrate its effectiveness. Furthermore, CAM exhibits superiority over the state-of-the-art method by an average performance gain of 4.1% across six more challenging datasets, despite utilizing a smaller model size. Our study highlights the importance of incorporating canonical mask guidance and aligned feature refinement techniques for robust scene text recognition. The code is available at https://github.com/MelosY/CAM., Comment: Accepted by Pattern Recognition

Scene text recognition (STR) is a challenging task that requires large-scale annotated data for training. However, collecting and labeling real text images is expensive and time-consuming, which limits the availability of real data. Therefore, most existing STR methods resort to synthetic data, which may introduce domain discrepancy and degrade the performance of STR models. To alleviate this problem, recent semi-supervised STR methods exploit unlabeled real data by enforcing character-level consistency regularization between weakly and strongly augmented views of the same image. However, these methods neglect word-level consistency, which is crucial for sequence recognition tasks. This paper proposes a novel semi-supervised learning method for STR that incorporates word-level consistency regularization from both visual and semantic aspects. Specifically, we devise a shortest path alignment module to align the sequential visual features of different views and minimize their distance. Moreover, we adopt a reinforcement learning framework to optimize the semantic similarity of the predicted strings in the embedding space. We conduct extensive experiments on several standard and challenging STR benchmarks and demonstrate the superiority of our proposed method over existing semi-supervised STR methods., Comment: Accepted by Pattern Recognition Letters

This paper presents a deep learning enhanced adaptive unscented Kalman filter (UKF) for predicting human arm motion in the context of manufacturing. Unlike previous network-based methods that solely rely on captured human motion data, which is represented as bone vectors in this paper, we incorporate a human arm dynamic model into the motion prediction algorithm and use the UKF to iteratively forecast human arm motions. Specifically, a Lagrangian-mechanics-based physical model is employed to correlate arm motions with associated muscle forces. Then a Recurrent Neural Network (RNN) is integrated into the framework to predict future muscle forces, which are transferred back to future arm motions based on the dynamic model. Given the absence of measurement data for future human motions that can be input into the UKF to update the state, we integrate another RNN to directly predict human future motions and treat the prediction as surrogate measurement data fed into the UKF. A noteworthy aspect of this study involves the quantification of uncertainties associated with both the data-driven and physical models in one unified framework. These quantified uncertainties are used to dynamically adapt the measurement and process noises of the UKF over time. This adaption, driven by the uncertainties of the RNN models, addresses inaccuracies stemming from the data-driven model and mitigates discrepancies between the assumed and true physical models, ultimately enhancing the accuracy and robustness of our predictions. Compared to the traditional RNN-based prediction, our method demonstrates improved accuracy and robustness in extensive experimental validations of various types of human motions.

With the rapid development of blockchain and its applications, the amount of data stored on decentralized storage networks (DSNs) has grown exponentially. DSNs bring together affordable storage resources from around the world to provide robust, decentralized storage services for tens of thousands of decentralized applications (dApps). However, existing DSNs do not offer verifiability when implementing erasure coding for redundant storage, making them vulnerable to Byzantine encoders. Additionally, there is a lack of Byzantine fault-tolerant consensus for optimal resilience in DSNs. This paper introduces BFT-DSN, a Byzantine fault-tolerant decentralized storage network designed to address these challenges. BFT-DSN combines storage-weighted BFT consensus with erasure coding and incorporates homomorphic fingerprints and weighted threshold signatures for decentralized verification. The implementation of BFT-DSN demonstrates its comparable performance in terms of storage cost and latency as well as superior performance in Byzantine resilience when compared to existing industrial decentralized storage networks., Comment: 11 pages, 8 figures

In recent years, large language models have achieved significant success in generative tasks (e.g., speech cloning and audio generation) related to speech, audio, music, and other signal domains. A crucial element of these models is the discrete acoustic codecs, which serves as an intermediate representation replacing the mel-spectrogram. However, there exist several gaps between discrete codecs and downstream speech language models. Specifically, 1) most codec models are trained on only 1,000 hours of data, whereas most speech language models are trained on 60,000 hours; 2) Achieving good reconstruction performance requires the utilization of numerous codebooks, which increases the burden on downstream speech language models; 3) The initial channel of the codebooks contains excessive information, making it challenging to directly generate acoustic tokens from weakly supervised signals such as text in downstream tasks. Consequently, leveraging the characteristics of speech language models, we propose Language-Codec. In the Language-Codec, we introduce a Mask Channel Residual Vector Quantization (MCRVQ) mechanism along with improved Fourier transform structures and larger training datasets to address the aforementioned gaps. We compare our method with competing audio compression algorithms and observe significant outperformance across extensive evaluations. Furthermore, we also validate the efficiency of the Language-Codec on downstream speech language models. The source code and pre-trained models can be accessed at https://github.com/jishengpeng/languagecodec ., Comment: We release a more powerful checkpoint in Language-Codec v3

State-of-the-art large language models (LLMs) are now claiming remarkable supported context lengths of 256k or even more. In contrast, the average context lengths of mainstream benchmarks are insufficient (5k-21k), and they suffer from potential knowledge leakage and inaccurate metrics, resulting in biased evaluation. This paper introduces LV-Eval, a challenging long-context benchmark with five length levels (16k, 32k, 64k, 128k, and 256k) reaching up to 256k words. LV-Eval features two main tasks, single-hop QA and multi-hop QA, comprising 11 bilingual datasets. The design of LV-Eval has incorporated three key techniques, namely confusing facts insertion, keyword and phrase replacement, and keyword-recall-based metric design. The advantages of LV-Eval include controllable evaluation across different context lengths, challenging test instances with confusing facts, mitigated knowledge leakage, and more objective evaluations. We evaluate 10 LLMs on LV-Eval and conduct ablation studies on the techniques used in LV-Eval construction. The results reveal that: (i) Commercial LLMs generally outperform open-source LLMs when evaluated within length levels shorter than their claimed context length. However, their overall performance is surpassed by open-source LLMs with longer context lengths. (ii) Extremely long-context LLMs, such as Yi-6B-200k, exhibit a relatively gentle degradation of performance, but their absolute performances may not necessarily be higher than those of LLMs with shorter context lengths. (iii) LLMs' performances can significantly degrade in the presence of confusing information, especially in the pressure test of "needle in a haystack". (iv) Issues related to knowledge leakage and inaccurate metrics introduce bias in evaluation, and these concerns are alleviated in LV-Eval. All datasets and evaluation codes are released at: https://github.com/infinigence/LVEval.

It is now commonplace for organizations to pay developers to work on specific open source software (OSS) projects to pursue their business goals. Such paid developers work alongside voluntary contributors, but given the different motivations of these two groups of developers, conflict may arise, which may pose a threat to a project's sustainability. This paper presents an empirical study of paid developers and volunteers in Rust, a popular open source programming language project. Rust is a particularly interesting case given considerable concerns about corporate participation. We compare volunteers and paid developers through contribution characteristics and long-term participation, and solicit volunteers' perceptions on paid developers. We find that core paid developers tend to contribute more frequently; commits contributed by one-time paid developers have bigger sizes; peripheral paid developers implement more features; and being paid plays a positive role in becoming a long-term contributor. We also find that volunteers do have some prejudices against paid developers. This study suggests that the dichotomous view of paid vs. volunteer developers is too simplistic and that further subgroups can be identified. Companies should become more sensitive to how they engage with OSS communities, in certain ways as suggested by this study.

Location-based services (LBSs) in vehicular ad hoc networks (VANETs) offer users numerous conveniences. However, the extensive use of LBSs raises concerns about the privacy of users' trajectories, as adversaries can exploit temporal correlations between different locations to extract personal information. Additionally, users have varying privacy requirements depending on the time and location. To address these issues, this paper proposes a personalized trajectory privacy protection mechanism (PTPPM). This mechanism first uses the temporal correlation between trajectory locations to determine the possible location set for each time instant. We identify a protection location set (PLS) for each location by employing the Hilbert curve-based minimum distance search algorithm. This approach incorporates the complementary features of geo-indistinguishability and distortion privacy. We put forth a novel Permute-and-Flip mechanism for location perturbation, which maps its initial application in data publishing privacy protection to a location perturbation mechanism. This mechanism generates fake locations with smaller perturbation distances while improving the balance between privacy and quality of service (QoS). Simulation results show that our mechanism outperforms the benchmark by providing enhanced privacy protection while meeting user's QoS requirements.

Federated learning is a powerful technique that enables collaborative learning among different clients. Prototype-based federated learning is a specific approach that improves the performance of local models under non-IID (non-Independently and Identically Distributed) settings by integrating class prototypes. However, prototype-based federated learning faces several challenges, such as prototype redundancy and prototype failure, which limit its accuracy. It is also susceptible to poisoning attacks and server malfunctions, which can degrade the prototype quality. To address these issues, we propose FedRFQ, a prototype-based federated learning approach that aims to reduce redundancy, minimize failures, and improve \underline{q}uality. FedRFQ leverages a SoftPool mechanism, which effectively mitigates prototype redundancy and prototype failure on non-IID data. Furthermore, we introduce the BFT-detect, a BFT (Byzantine Fault Tolerance) detectable aggregation algorithm, to ensure the security of FedRFQ against poisoning attacks and server malfunctions. Finally, we conduct experiments on three different datasets, namely MNIST, FEMNIST, and CIFAR-10, and the results demonstrate that FedRFQ outperforms existing baselines in terms of accuracy when handling non-IID data.

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5.

This paper presents policy-based motion planning for robotic systems. The motion planning literature has been mostly focused on open-loop trajectory planning which is followed by tracking online. In contrast, we solve the problem of path planning and controller synthesis simultaneously by solving the related feedback control problem. We present a novel incremental policy (iPolicy) algorithm for motion planning, which integrates sampling-based methods and set-valued optimal control methods to compute feedback controllers for the robotic system. In particular, we use sampling to incrementally construct the state space of the system. Asynchronous value iterations are performed on the sampled state space to synthesize the incremental policy feedback controller. We show the convergence of the estimates to the optimal value function in continuous state space. Numerical results with various different dynamical systems (including nonholonomic systems) verify the optimality and effectiveness of iPolicy.

In this paper, we present ControlSpeech, a text-to-speech (TTS) system capable of fully cloning the speaker's voice and enabling arbitrary control and adjustment of speaking style, merely based on a few seconds of audio prompt and a simple textual style description prompt. Prior zero-shot TTS models and controllable TTS models either could only mimic the speaker's voice without further control and adjustment capabilities or were unrelated to speaker-specific voice generation. Therefore, ControlSpeech focuses on a more challenging new task-a TTS system with controllable timbre, content, and style at the same time. ControlSpeech takes speech prompts, content prompts, and style prompts as inputs and utilizes bidirectional attention and mask-based parallel decoding to capture corresponding codec representations in a discrete decoupling codec space. Moreover, we discovered the issue of text style controllability in a many-to-many mapping fashion and proposed the Style Mixture Semantic Density (SMSD) model to resolve this problem. SMSD module which is based on Gaussian mixture density networks, is designed to enhance the fine-grained partitioning and sampling capabilities of style semantic information and generate speech with more diverse styles. In terms of experiments, we make available a controllable model toolkit called ControlToolkit with a new style controllable dataset, some replicated baseline models and propose new metrics to evaluate both the control capability and the quality of generated audio in ControlSpeech. The relevant ablation studies validate the necessity of each component in ControlSpeech is necessary. We hope that ControlSpeech can establish the next foundation paradigm of controllable speech synthesis. The relevant code and demo are available at https://github.com/jishengpeng/ControlSpeech .

Near-space airship-borne communication network is recognized to be an indispensable component of the future integrated ground-air-space network thanks to airships' advantage of long-term residency at stratospheric altitudes, but it urgently needs reliable and efficient Airship-to-X link. To improve the transmission efficiency and capacity, this paper proposes to integrate semantic communication with massive multiple-input multiple-output (MIMO) technology. Specifically, we propose a deep joint semantic coding and beamforming (JSCBF) scheme for airship-based massive MIMO image transmission network in space, in which semantics from both source and channel are fused to jointly design the semantic coding and physical layer beamforming. First, we design two semantic extraction networks to extract semantics from image source and channel state information, respectively. Then, we propose a semantic fusion network that can fuse these semantics into complex-valued semantic features for subsequent physical-layer transmission. To efficiently transmit the fused semantic features at the physical layer, we then propose the hybrid data and model-driven semantic-aware beamforming networks. At the receiver, a semantic decoding network is designed to reconstruct the transmitted images. Finally, we perform end-to-end deep learning to jointly train all the modules, using the image reconstruction quality at the receivers as a metric. The proposed deep JSCBF scheme fully combines the efficient source compressibility and robust error correction capability of semantic communication with the high spectral efficiency of massive MIMO, achieving a significant performance improvement over existing approaches., Comment: Major Revision by IEEE JSAC

Digital watermarking methods are commonly used to safeguard digital media copyrights by confirming ownership and deterring unauthorized use. However, without reliable third-party oversight, these methods risk security vulnerabilities during watermark extraction. Furthermore, digital media lacks tangible ownership attributes, posing challenges for secure copyright transfer and tracing. This study introduces DataSafe, a copyright protection scheme that combines physical unclonable functions (PUFs) and blockchain technology. PUF devices use their unique fingerprints for blockchain registration. Subsequently, these devices incorporate invisible watermarking techniques to embed digital watermarks into media for copyright protection. The watermark verification process is confined within the devices, preserving confidentiality during extraction, validating identities during copyright exchanges, and facilitating blockchain-based traceability of copyright transfers. The implementation of a prototype system on the LPC55S69-EVK development board is detailed, illustrating the practicality and effectiveness of the proposed solution.